Mathieu Blanchette

The paper shows that H3K27me3 spreading regulates chromatin architecture and gene repression, with implications for cell fate and tumor development.

The paper develops a genome assembly pipeline to analyze Borrelia burgdorferi strains, revealing genomic diversity at telomeres and plasmids across Canadian isolates.

This study shows Leishmania parasites use extrachromosomal DNA amplification to adapt to drug pressure, with linear and circular amplicons forming through recombination events.

Polaris is a tool for chromatin loop annotation in bulk and single-cell Hi-C data that outperforms existing methods across different datasets and conditions.

This paper introduces DendroPRS, a machine learning method that improves genotype-to-phenotype prediction across diverse ancestries by incorporating phylogenetic relationships.

The paper improves RNA secondary structure prediction by integrating evolutionary information through pretraining and a scalable neural architecture, achieving better performance on various benchmarks.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

SACSANN predicts chromosomal compartments from genomic sequence features, showing transferability across human and mouse cell types.

This study introduces RPI-Net, a graph neural network for predicting RNA-protein interactions, showing improved performance and addressing sequence bias in CLIP-Seq data.

RADICL-seq maps genome-wide RNA-chromatin interactions in intact nuclei, revealing general and cell type-specific patterns of transcript occupancy and the role of transcription in chromatin structure.

This paper introduces HIFI, a method to estimate DNA-DNA interaction frequencies at restriction-fragment resolution from Hi-C data, improving resolution and biological insights.

This paper introduces 3DGV, a VR platform for exploring 3D genome structures, aiming to improve interpretation of complex genomic spatial data.

RobusTAD scores TAD boundaries robustly across varying experimental conditions, enabling comparative analysis of TAD structures.

The paper introduces ECLUST, a framework that improves predictive modeling in high-dimensional data by incorporating exposure-dependent clustering, showing better performance than traditional methods.